5646 results about "Biologic marker" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

The invention provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The present disclosure, in some aspects, provides one or more disease state-associated genes for repeat expansion diseases (e.g., a repeat expansion disease associated with spliceopathy, such as DM1). In some embodiments, each of the disease-state associated gene described herein is a disease-state associated gene for a repeat expansion disease (e.g., DM1). Methods of measuring the splicing activity of disease state- associated genes, e.g., for determining disease state and monitoring patient as well as treatment efficacy are also provided.

The invention discloses a breast cancer immunohistochemical index and typing prediction method and system. The prediction method comprises the following steps: acquiring a breast ultrasound image and a breast magnetic resonance image of a patient; performing preprocessing and quality control on the mammary gland ultrasonic image and the mammary gland magnetic resonance image; performing breast lesion area segmentation by adopting a deep learning model to obtain segmented lesion areas; based on the segmented lesion area, extracting multi-modal radiomics characteristics of the breast ultrasonic image and the breast magnetic resonance image; fusing the multi-modal radiomics characteristics of the breast ultrasound image and the breast magnetic resonance image, constructing a machine learning model, and performing immunohistochemical index prediction to obtain an immunohistochemical index prediction result; and performing breast cancer molecular typing analysis according to the immunohistochemical index prediction result. By fusing the multi-modal image information, the tumor features can be described more comprehensively, and the accuracy of biomarker prediction is improved.

The invention discloses a combination of different nucleic acid aptamers with known 291 targets, sequences of the nucleic acid aptamers of the combination are subjected to sequencing analysis through a single-cell high-throughput sequencing technology, and a cell map aiming at peripheral blood-derived mononuclear cells of a biliary tract cancer patient can be constructed. The nucleic acid aptamers covered by the combination have high specific affinity to molecular targets of the nucleic acid aptamers, and after the two ends of the nucleic acid aptamer sequences extend to adapt to joints of a commercial sequencing platform, high-throughput sequencing is carried out on the nucleic acid aptamer sequences which are combined with the surfaces of mononuclear cells and are provided with the sequencing joints; high-throughput analysis of the biomarkers on the surface of the peripheral blood-derived mononuclear cell of a biliary tract cancer patient can be realized, so that the cell map of the peripheral blood-derived mononuclear cell of the biliary tract cancer is constructed.

The invention relates to the technical field of biomarkers, in particular to a blood biomarker combination for prediction and auxiliary diagnosis of ovarian reserve hypofunction (DOR), a corresponding detection kit and application of the blood biomarker combination. According to the invention, the content change of the nicotinamide mononucleotide (NMN) and the metabolite combination thereof in serum or plasma is screened for the first time and can be used as a detection index for predicting the ovarian reserve function decline. Based on the biomarker combination, the invention further provides an in-vitro detection kit containing a related standard substance, an internal standard solution and an extracting solution, and a data analysis method combined with a random forest model, so that an individual with a normal ovarian reserve function and an individual with hypofunction can be accurately distinguished. The method is noninvasive, simple, convenient, high in sensitivity and suitable for early recognition and layered screening of clinical ovarian reserve hypofunction.

Platinum-acridines and analogs thereof as cytotoxic agents for cancer treatment. Also provided methods of using hMATE1 (SLC47A1) as a biomarker to identify tumors that are likely to respond to the agents, and epigenetically sensitizing tumor tissue to anticancer drugs targeting this membrane transporter.

The invention discloses an enteritis patient condition severity risk early warning method, a medium and equipment. The method comprises the following steps: acquiring a borborygmus original signal and a clinical multi-dimensional physiological parameter sequence through a sensing device; constructing a borborygmus dynamic characteristic spectrum based on the borborygmus original signal to generate an acoustic biomarker time sequence; inputting the acoustic biomarker time sequence and the clinical multi-dimensional physiological parameter sequence into a multi-modal fusion early warning model to obtain an intestinal inflammation risk index; executing a signal quality self-evaluation process and generating a data quality warning code when the signal quality is abnormal; triggering a multi-node collaborative monitoring mechanism based on the risk index to generate an intestinal state multi-dimensional situation map; establishing an individualized risk baseline and generating a graded early warning instruction; and finally outputting a comprehensive early warning report. According to the method, multi-modal fusion analysis of the borborygmus signal and the clinical parameters is realized, the accuracy and timeliness of illness state early warning are remarkably improved through dynamic risk assessment and signal quality monitoring, and a reliable basis is provided for clinical decision making.

An information processing apparatus (100) according to one embodiment of the present disclosure includes a guide image generation unit (133) configured to sum up a plurality of images each including spectral information regarding a biomarker, and perform a division on a result by a number of summed images to generate a guide image for correction.

The invention relates to the technical field of cardiovascular medicine, in particular to an automatic alarm system for cardiovascular medicine, which comprises a biomarker analysis module, a physiological parameter analysis module, a cardiovascular risk assessment module, a cardiovascular early warning adjustment module and an alarm trigger notification module. According to the invention, by integrating real-time monitoring of blood biomarkers and cardiovascular physiological parameters, the early recognition capability of cardiovascular disease risks is improved, key biomarkers are dynamically measured and compared with historical marker data, and subtle physiological changes can be captured at the early stage of asymptomatic conditions, so that potential cardiovascular events are warned; through correlation analysis of electrocardiogram, blood pressure and pulse waveforms, the system can accurately draw a comprehensive image of cardiovascular functions, so that more targeted monitoring and intervention are implemented, timeliness and accuracy of medical response are ensured, doctors can formulate targeted monitoring schemes according to real-time data, and the monitoring efficiency is improved. Therefore, deterioration or occurrence of cardiovascular events of patients can be effectively prevented.

Provided herein are methods and systems for diagnosing pathological conditions using machine learning and artificial intelligence models. An exemplary method can include loading an abundance matrix that represents mass spectrometry reads of a sample of a plurality of volatile organic compounds (VOCs) extracted from a biological sample. The abundance matrix can represent each of the plurality of VOCs in a mass-to-charge ratio dimension, an abundance dimension, and a retention time dimension. The method can include providing the abundance matrix to a machine-learning model. The machine- learning model can be trained on abundance matrixes of biomarkers of VOCs collected from healthy and diseased subjects. The method can include receiving, from the machine-learning model, a prediction of a state of a disease state.

The invention belongs to the field of electrochemical biosensors, discloses a preparation method of an electrochemical biosensor capable of being used for multi-mode sweat detection, and particularly provides a single-electrode multifunctional arrangement method in a three-electrode system. A sensor material with high sensitivity and high selectivity is prepared, then the sensor material is printed through screen printing and assembled into a portable device to achieve simultaneous detection of multiple biomarkers in sweat, and the preparation method comprises the steps of selection and modification of an electrode material, sensor assembly and integration of a signal acquisition and analysis system. Through innovative material design and process optimization, the sensor not only can acquire information of various biomarkers in sweat in real time, but also can provide reliable data analysis, and shows excellent long-term stability and anti-interference capability in practical application. The technology provides an effective and reliable solution for real-time health monitoring.

A personalized diagnostic system for healthcare, comprehensive: a deep learning framework trained on a diverse dataset of medical images to analyze and interpret medical images; a data fusion module that integrates imaging data with non-imaging patient data such as electronic health records (EHRs), genetic information, and biomarker profiles; a high-performance computing system that uses GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) for real-time and batch processing of medical data; an intuitive user interface that displays diagnostic results, visualizations of medical images, and interactive functions for adjusting diagnostic parameters; a continuous learning mechanism that updates the deep learning models based on new data and expert feedback; a cloud-based platform for managing the continuous learning process, including the storage of anonymized patient data and diagnostic results; an explanatory module that provides detailed explanations of the diagnostic process using techniques such as Grad-CAM (Gradient-weighted Class Activation Mapping) and SHAP (SHapley Additive exPlanations); and a role-based access control (RBAC) mechanism to secure patient data.

The disclosure provides technologies that permit detection and / or characterization of brain biomarkers (e.g., disease-associated biomarkers) in non-CNS samples, including by liquid biopsy (e.g., blood, serum, etc.). Among other things, the present disclosure demonstrates that ultrasound opening of the blood brain barrier can achieve detectable increases in brain-derived materials (e.g., brain-derived proteins, neuron-derived extracellular vesicles, and / or cell-free DNA) in readily-sampled systemic liquids.

The invention relates to an ovarian reserve hypofunction prediction method based on multi-data fusion, and the technical scheme comprises the following steps: regularly collecting biomarker data related to ovarian reserve, including anti-mullerian canal hormone AMH, follicle stimulating hormone FSH, luteinizing hormone LH, estradiol E2, and ultrasonic image data including follicle quantity and ovarian volume; acquiring dynamic change curve data and capturing the change trend of the ovarian reserve function when the acquisition time point covers the key stage of the menstrual cycle; analyzing the biomarker data acquired for multiple times by using an LSTM deep learning model, and modeling a change trend of the ovarian reserve function of each patient along with time; by analyzing the periodic fluctuation of AMH and FSH biomarkers, the decline time node of the ovarian reserve function in the future is predicted; and on the basis of the LSTM deep learning model of S1.1, performing personalized risk assessment in combination with individual characteristics of patients including age, genes, lifestyles and disease histories.

The invention relates to the technical field of biological medicine, and discloses a pharmaceutical composition of paclitaxel dimer prodrug nanoparticles and a traditional Chinese medicine compound and application of the pharmaceutical composition, and the pharmaceutical composition is composed of Qiyuansanlong prescription freeze-dried powder and paclitaxel dimer prodrug nanoparticles; wherein the paclitaxel dimer prodrug nanoparticles comprise one of NQO1 response type PTX dimer prodrug nanoparticles (PTX-Q-PTX NPs) or GSH (glutathione) response type PTX dimer prodrug nanoparticles (PTX-SeSe-PTXNPs), and the paclitaxel dimer prodrug nanoparticles comprise one of NQO1 response type PTX dimer prodrug nanoparticles (PTX-Q-PTX NPs) and GSH response type PTX dimer prodrug nanoparticles (PTX-SeSe-PTXNPs). According to the pharmaceutical composition disclosed by the invention, a traditional Chinese and western medicine synergistic treatment system is innovatively constructed, and the advantage of multi-dimensional synergistic interaction is shown. Firstly, an NQO1 / GSH double-response type intelligent release system is adopted, and a biomarker highly expressed by tumor tissue can be specifically responded to realize precise drug release, so that the PTX concentration in tumors is improved compared with that of a traditional dosage form, and meanwhile, the drug circulation time is prolonged. Secondly, the active ingredients in the Qiyuansanlong prescription freeze-dried powder can significantly down-regulate the PD-L1 expression level, synergistically enhance CD4 + T and CD8 + T cell infiltration, and form chemical-immune dual killing effects.

The invention discloses a pain management method based on bioelectrical impedance data and a machine learning algorithm, and relates to the technical field of medical health, and the method comprises the steps: carrying out the phase delay analysis of an impedance phase spectrum matrix, calculating the change rate of a phase angle of each frequency point along with time, and obtaining the maximum phase lag time; according to the temperature field sequence and the maximum phase lag time, time-space registration is carried out through a multi-modal data fusion method and impedance modulus changes of the corresponding areas, and a pain biomarker feature library is generated; through a bidirectional LSTM and a cross attention fusion method, time sequence modeling and cross-modal correlation analysis are carried out on bioelectricity signal characteristics extracted by a sliding window and historical pain data, a dynamic evolution rule of pain development is captured, a pain management triple containing pain intensity, intervention response and prognosis evaluation is generated, and a pain management result is obtained. And a quantitative basis is provided for personalized treatment.

The invention belongs to the technical field of medical pathological images, and particularly relates to a multi-instance multi-task learning pan cancer biomarker prediction method and system, and the method comprises the steps: obtaining a full-view digital pathological image and pan cancer biomarker expression data of a solid tumor patient; performing preprocessing and feature extraction based on the full-view digital pathological image to obtain a pathological feature data set; based on the pathological feature data set, inputting the pathological feature data set into a twin multi-example full convolutional network model for training to obtain pathological low-dimensional features, and calculating an attention score corresponding to each pathological feature in combination with a linear attention mechanism; and in combination with each pathologic low-dimensional feature and the corresponding attention score, calculating to obtain a pathologic depth feature, and inputting the pathologic depth feature into a multi-task classification prediction model for classification prediction to obtain a prediction result of each biomarker in the pan cancer.

Disclosed herein are methods, compositions, and devices for use in diagnosis and treatment of cancer. The methods include sequencing a panel of regions in cell-free nucleic acid molecules and detecting one or more biomarkers that are indicative of a cancer.

The invention relates to the technical field of aplastic anemia (AA) treatment research, discloses a biomarker method for predicting the curative effect of cyclosporine on aplastic anemia, and aims to detect plasma differential metabolites of patients with aplastic anemia by adopting a metabonomics technology, find potential biomarkers for treating AA patients and find the curative effect of cyclosporine on the AA patients. CsA) is used for treating the in-vivo metabolism of AA. The method is based on liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) technologies, and comprises the following steps: collecting 5 plasma samples of AA patients, 5 plasma samples of healthy people and 5 plasma samples of patients in a stable period after treatment for comparative analysis; and screening differential compounds by adopting a multivariate statistical method, and further performing enrichment analysis on differential metabolites. Research of the method shows that in-vivo metabolites of AA patients and healthy people are obviously different, and the way related to immunity and energy metabolism may be a key target for treating AA; csA intervenes the occurrence and development of AA by regulating and controlling immune-related metabolic ways.

The invention provides a hematologic tumor cord blood transplantation treatment prognosis risk assessment method and system based on multi-modal data fusion, a medium and electronic equipment, and relates to the technical field of medical artificial intelligence and clinical decision support systems. The method comprises the following steps: acquiring multi-modal data including unit characteristic data of cord blood of a patient, biomarker data and the like; screening out a simplified feature set from the preprocessed multi-modal data; fusing the simplified features based on three complementary strategies of feature level direct cascade, knowledge graph semantic association and multi-model decision integration; and a prediction model is constructed, and a standardized risk assessment report is generated based on the optimized hierarchical risk levels and is used for assisting clinical decision support. According to the method, accurate layering of the prognosis risk of the hematologic tumor umbilical cord blood transplantation patient is achieved, high-accuracy risk prediction is provided, good interpretability and personalized decision support capacity are achieved, and a scientific basis is provided for clinical practice.

The invention relates to the technical field of health risk assessment, in particular to an intelligent early warning system for postoperative infection risk of orthopedic trauma. The system comprises a data acquisition module for acquiring physiological feature data and biomarker data; the key monitoring and screening module screens key monitoring patients according to the continuous abnormal representation growth degree of the initial physiological signs; the infection risk assessment module obtains a marker level index by using the initial deviation level and the growth amplitude of the biomarker, and obtains a wound infection risk coefficient in combination with the prediction condition of historical biomarker data; and the early warning report generation module is used for dividing patient early warning levels according to the physiological parameter abnormality and the wound infection risk coefficient to generate a risk report. According to the method, the evaluation accuracy of postoperative infection is improved through the physiological sign abnormity continuity and the biomarker sudden increase and prediction trend, the subsequent early warning level and risk report generation are more reliable, and a more personalized and accurate treatment scheme can be provided for a patient.

The invention relates to a breast cancer plasma transporter miRNA marker and application thereof, and belongs to the technical field of biological detection. In the prior art, biomarkers for breast cancer liquid biopsy have the problems of low content, high heterogeneity, insufficient time resolution and the like, so that the detection reliability is limited. According to the invention, a specific miRNA (micro Ribonucleic Acid) marker (including one or more of let-7d-5p, miR-103a-3p and miR-22-3p) of the plasma transporter is provided. The marker can be used for preparing a breast cancer detection reagent or kit. Clinical verifications show that the miRNAs are remarkably and highly expressed in plasma migrators of breast cancer patients, the AUC value reaches 0.763-0.808, the sensitivity is 66.7%-86.7%, the specificity is 66.7%-88.3%, the miRNAs have the advantages of high signal-to-noise ratio and dynamic monitoring, and the accuracy and practicability of breast cancer detection are remarkably improved.

The invention belongs to the technical field of biological medicines, and particularly relates to application of secretory lactase-like protein (LCTL) as a diagnostic marker in preparation of a diagnostic kit for chronic kidney diseases. According to the present invention, the secretory form of the LCTL is found for the first time, the secretory LCTL content in the human serum is detected by using the existing commercial ELISA kit for the first time, and the further research finds that the secretory LCTL content in the serum of the chronic kidney disease patient is significantly reduced compared with the normal person; and the content of the LCTL in the serum is obviously related to the renal function impairment degree and proteinuria degree of the patient, so that the application of the secretory LCTL in preparing the biomarker for diagnosing the chronic kidney disease is provided for the first time. The invention provides a new method for chronic kidney disease diagnosis, and has important clinical significance and commercial value.

The invention discloses a bacterial polypeptide Gfo and application thereof in preparation of rheumatoid arthritis diagnosis products, and belongs to the technical field of rheumatoid arthritis diagnosis. The amino acid sequence of the bacterial polypeptide Gfo is LNSHGFLPETE. A diagnostic product prepared from the bacterial polypeptide Gfo can effectively detect that the level of an anti-bacterial polypeptide Gfo antibody in a biological sample of a rheumatoid arthritis patient is obviously higher than that of healthy people and other rheumatoid immune diseases similar to rheumatoid arthritis. In addition, in CCP antibody and RF negative patients, the increase is still significant. Therefore, the antibacterial polypeptide Gfo antibody is an important biomarker of rheumatoid arthritis, and provides an effective new method for diagnosis of RA, especially diagnosis of serum-negative RA.

Disclosed herein are methods of treatment and methods of evaluating individuals to identify and measure biomarkers and / or states of the individuals indicative of drug responsiveness to one or more 5-HT receptor agonist.

The invention discloses a biomarker combination for predicting calendar age, a model and application of the biomarker combination. 57 kinds of DNA methylated CpG sites screened from many elderly subjects are creatively introduced, through multiple detection and large-scale parallel DNA sequencing, operation is easy, the result is highly repeatable, and the age prediction model constructed based on the method is good in stability and accuracy and high in sensitivity and has good application prospects. Calendar age can be predicted from trace blood samples associated with forensic cases.

The invention belongs to the technical field of biology, and relates to a biomarker for early diagnosis of non-small cell lung cancer and application of the biomarker. The biomarker comprises a combination of secretory phosphoprotein 1, epithelial cell adhesion molecules and matrix metallopeptidase 14, and / or a combination of coding nucleic acid of the secretory phosphoprotein 1, coding nucleic acid of the epithelial cell adhesion molecules and coding nucleic acid of the matrix metallopeptidase 14. The coding nucleic acid comprises mRNA or cDNA. In a test set, a non-small cell lung cancer circulating tumor cell scoring diagnosis model constructed by taking the biomarker as the characteristic has the diagnosis accuracy of 87.5% and the sensitivity and specificity of 92.5% and 82.5% respectively. Compared with existing clinical tumor indexes, cancer can be found more early, and early screening of the non-small cell lung cancer is facilitated.

The invention relates to the technical field of intelligent diagnosis systems for neurodegenerative diseases, and discloses a system and equipment for identifying an early biomarker of Parkinson's disease based on non-invasive electroencephalogram. The Parkinson's disease early-stage biomarker identification system comprises a multi-mode signal acquisition module which is provided with a 64-lead dry electrode electroencephalogram cap, an integrated preamplifier and an ADC converter and is used for synchronously acquiring resting-state electroencephalogram and event-related potential, dynamically completing switching of the resting-state electroencephalogram sampling rate and transmitting acquired original data to a preprocessing module. According to the method, revolutionary improvement is realized in three dimensions of acquisition, analysis and calculation through system-level collaborative innovation, a nanocrystalline shielding dry electrode is combined with a dynamic impedance adjustment technology, so that the signal-to-noise ratio of a home environment is increased to 28dB, and the pathological correlation of key markers such as frontal lobe-basal node loop gamma entropy is verified by Granger causality and has a good application prospect. The cloud distributed model server supports 2000 paths of concurrent analysis, and the real-time bottleneck of home screening is thoroughly solved.

A mask-based diagnostic apparatus is provided for detecting a biomarker contained in exhaled breath of a test subject. An exhaled breath condensate (EBC) collector converts breath vapor received from the lungs and airways of the test subject into a fluid biosample. The EBC collector including a thermal mass, a condensate-forming surface and a fluid conductor disposed on the condensate-forming surface. A fluid transfer system receives the fluid biosample from the EBC collector. A biomarker testing unit receives the fluid biosample from the fluid transfer system and tests the fluid biosample for a target biomarker. A testing system support is provided for supporting the EBC collector, the fluid transfer system and the biomarker testing unit. The testing system support is configured and dimensioned to fit inside a face mask. A face mask is provided forming an exhaled breath vapor containment volume to hold the exhaled breath vapor in proximity to the EBC collector to enable the condensate-forming surface cooled by the thermal mass to coalesce the exhaled breath vapor into the fluid biosample.